Morphology identification in tissue samples based on comparison to named feature vectors

ABSTRACT

Locating morphology in a tissue sample is achieved with devices and methods involving storage of a plurality of feature vectors, each associated with a specific named superpixel of a larger image of a tissue sample from a mammalian body. A microscope outputs, in some embodiments, a live image of an additional tissue sample or a digitized version of the output is used. At least one superpixel of the image is converted into a feature vector and a nearest match between the first feature vector and the plurality of stored feature vectors is made. A first name suggestion is then made based on the nearest match comparison to a store feature vector. Further, regions of interest within the image can be brought to a viewer&#39;s attention based on their past history of selection, or that of others.

FIELD OF THE DISCLOSED TECHNOLOGY

The disclosed technology relates generally to processing histologyslides, and more specifically to a method of learning and determiningmorphology or pathology exited on the slides.

BACKGROUND OF THE DISCLOSED TECHNOLOGY

Identification of morphology and/or pathologies is often carried out bytaking tissue samples, cultures, or the like and viewing same under amicroscope. Histology slides, such as Hematoxylin and Eosin slides(herein, “H&E”) are viewed to determine a cause of illness or detect anillness, presence of a pathogen, or abnormality in a body of a human orother mammal. When viewing a slide, it is easy to miss an item ofinterest when one is not actively looking for the specific item ofinterest (such as a pathology) or the variation is slight or small. Whenviewing a slide covering a large area where one must “scroll” to viewunder a microscope, it is further easy to miss an important detail.

The prior art has made some attempts to automate the process, butautomation comes with risk of false positives or making the system lessefficient. Examples of can be seen in U.S. Pat. No. 8,488,863, U.S. Pat.No. 8,897,537, U.S. Pat. No. 8,934,718 and EP 3,140,778 A2, whereregions of interest have to be manually identified. For example, themethod described in U.S. Pat. No. 8,488,863 requires classification ofeach pixel of a slide which requires hours of processing per slide. Mostimportantly, it is not clear that individual pixels provide sufficientpredictive power to identify a pattern which is highly predictive of adisease state.

The method described in U.S. Pat. No. 8,897,537 and U.S. Pat. No.8,934,718 requires that one select region of a slide to be analyzed byan automated process, but such a method leaves intact a problem of humanerror or missed information. There is a low (almost no) concordanceamong human pathologists, as shown by Ezgi Mercan, Selim Aksoyy, LindaG. Shapiro, Donald L. Weaverx, Tad Brunye, Joann G. Elmorez,“Localization of Diagnostically Relevant Regions of Interest in WSI”.Further, while it is mentioned that different analysis types may requiredifferent models, the disclosure of U.S. Pat. No. 8,897,537 it isunknown, based on the disclosure in this prior art reference, how it ispossible for the system or a human to select the appropriate model toapply for each one of the tens of thousands of clinical contexts, e.g.,ICD10 codes.

The method described EP 3,140,778 A2 requires that the image analysis isperformed only after a region of interest has been identified. Further,it relies on predetermined thresholds to perform the analysis. Themethods described in U.S. Pat. No. 8,319,793 rely on object networks toguide the pixel classifier so as to overcome the limited predictivevalue of individual pixels. The methods described in U.S. Pat. No.8,319,793 relies on image preprocessing steps without which theeffectiveness of the method degrades dramatically.

What is needed in the art is to identify histologies and regions ofinterest both accurately and quickly. It is desired to take the bestthat a human can do in making identifications and diagnoses when viewinga slide, as well as improve upon or augment same with the ability of aprocessor to suggest or recommend same.

SUMMARY OF THE DISCLOSED TECHNOLOGY

The disadvantages of the prior art are solved or lessened based on amethod of helping to suggest or automatically identify morphologies in atissue sample. This is carried out by way of receiving or obtainingmultiple tissue samples from a mammal (e.g. human). These tissue samplesare viewed in real time using a microscope, dried on a slide, and/ordigitized into images. Then, based on receiving selections ofsuperpixels (blocks of 30×30, 100×100, 200×200 or another amount ofpixels, such as a larger block in high resolution image), thesuperpixels are named based on their morphology. For purposes of thisdisclosure, morphology is defined as “taxonomic classification of astructure within an organism.” The morphology can include modificationsto a structure based on the existence of, or being a foreign object orpathogen. Thus, the morphology can include a pathology. Pathology, forpurposes of this disclosure, is defined as “the structural andfunctional deviations from the normal that constitute disease orcharacterize a particular disease.”

Once the superpixel has a named morphology, it is also associated with afeature vector. A feature vector is defined as “an n-dimensional vectorof numerical features that represent a superpixel.” The calculation ofthe feature vector from the superpixel can take place before or afterthe morphology is named. A library or database of feature vectors iscreated, each associated with a superpixel and named morphology (orpathology). Then, when another superpixel is extracted from an image(defined as, “read into memory” or “calculated into it's own featurevector”), a nearest match is found between a new feature vector createdthere-from and a feature vector in the database or library. A nearestmatch is then made between the new feature vector and one of the featurevectors in the library/database.

An indication is then made to a viewer that a superpixel may beassociated with/representative of the named morphology which has alreadybeen associated with the feature vector which was determined to be thenearest match. A confirmation can then be received from the viewer thatthis extracted superpixel is, in fact, showing the same named morphologyas previously named and the system, as a whole is said to “learn” moreabout the named morphology and provide better and better matches in thefuture. After, for a particular feature vector and nearest matchesthere-to, a name has been confirmed a threshold minimum number of times,this name becomes unchangeable or permanent. Thus, future scannedsuperpixels cannot be named otherwise, or at least, such a name for themorphology cannot be edited by viewers of the system. (Unchangeable isdefined as requiring administrator privileges, in order to change thenamed morphology in the future, to the exclusion of the “viewer” or“views” who are carrying out some or all of the steps of the methodsclaimed in this technology.) However, before the threshold has beenreached and a particular morphology/feature vector set with the namedmorphology has not been named the threshold number of times, then thename can still be changed by viewers of the superpixels. The thresholdnumber of times before a name of a morphology becomes locked can dependon how close the feature vectors of the named morphology are to eachother, and/or can be a minimum number of 5, 8, 10, or 15 times aparticular morphology has been named.

In some cases, it can be possible to find/extract multiple superpixels(either by way of automatic conversion of superpixels into featurevectors, or based on viewer selections of superpixels) which areconverted into feature vectors which have not yet been named. Forexample, three different feature vectors can be created from threedifferent superpixels which are from one, two, or three different tissuesamples. These three feature vectors are found to be nearest matches toeach other (by way of the transitive property or as compared to otherfeature vectors stored in the database which are further awaythere-from). The superpixels associated therewith one or more of theseas yet unnamed morphologies can then be presented or exhibited and aname requested from a viewer. Once a name is provided for one of thefeature vectors, the name is then applied to the other feature vectorsfound to be the nearest match there-to.

The above method, in whole or in part, and any or all of the stepsdescribed in the preceding four paragraphs can be carried out multipletimes with a same viewer, each time with yet another tissue sampleand/or yet another superpixel and corresponding feature vector. In theprocess, patterns of interest can be determined for a particular viewerbased on one, two or all three of: a) areas of the images where the userzooms; b) areas of the image where the user pans; and/or c) areas of theimages where the user names regions. So too, patterns of interest can bedetermined based on overlapping interest (any of “a”, “b”, or “c” orparts thereof) between two viewers such that what is a “pattern ofinterest” for the first viewer is considered as such for the secondviewer.

Such “patterns of interest” between two users can also be determined asoverlapping and presented to additional users based on an input that thetwo users are in the same medical specialty. A “medical specialty”, forpurposes of this disclosure, is defined as one of the specialtiesdelineated by the Association of American Medical Colleges ofWashington, D.C. as delineated athttps://www.aamc.org/cim/specialty/exploreoptions/list/ as of June 2017or as updated periodically. Once a pattern of interest is known, inanother image of a tissue, suggestions of superpixels to view can besent to the viewer(s) based on their interest. Thus, a carcinoma mightbe suggested to an oncologist and a bone fracture might be suggested toan orthopedic surgeon.

In some embodiments, a section of an image of a tissue sample with ahigh density of named morphologies is suggested for viewing. That is,the superpixels are each converted into feature vectors and those thatare clustered or close together with the most named regions are broughtto the attention of a viewer as a place to direct one's focus orattention. Such a suggestion can be made by zooming in a display of thetissue sample to the high density section, outlining the high densitysection, and or color coding the high density section.

When, in some embodiments, a plurality of named morphologies in a tissuesample are determined to be related to a clinical context, an indicationthat a particular type of medical specialty should be employed for apatient associated with the tissue sample is made. For example, ageneral practitioner viewing a tissue sample might be suggested to referthe patient to a breast cancer specialist when many named regions in atissue sample are found to correspond to known type of breast cancer,based on the named morphologies of the associated nearest match featurevectors to the feature vectors created from the present tissue sample.”

The above can be carried it on real-time (defined as “as fast as able tobe processed by a processor and viewer carrying out steps disclosedherein”) with a digitized view of a tissue sample, as seen by amicroscope (a device which magnifies the size of images/received lightreflected off a tissue sample). Superpixels of a live image areconverted into a first feature vector, a nearest match between the firstfeature vector and a plurality of stored feature vectors is made, andoutput a first name suggestion of the at least one superpixel of thelive image based on the nearest match to one of the stored plurality offeature vectors associated with said specific named superpixel is made.Then, a viewer can input confirmations of the name suggestion and thename can be locked in (unchangeable) after it is so named a thresholdminimum number of times. Further, the pattern of interest featuresdescribed above can also apply, alerting the viewer to stop moving andfocus on a particular region (combination of superpixels) or particularsuperpixel.

Morphologies, or areas of greatest interest or greatest frequency whichcover a largest part of an image, or at least a plurality of superpixelscan be determined. A user is prompted or asked to name such morphologiesof greatest frequency in some embodiments of the disclosed technology.In some embodiments, any unnamed region or section of image which hasthere-within more than one superpixel are sent to a user to be named,such as by highlighting or listing the regions to be named and as yetremain unnamed.

Any device or step to a method described in this disclosure can compriseor consist of that which it is a part of, or the parts which make up thedevice or step. The term “and/or” is inclusive of the items which itjoins linguistically and each item by itself. “Substantially” is definedas “at least 95% of the term being described” and any device or aspectof a device or method described herein can be read as “comprising” or“consisting” thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic block diagram of a system or a device forlocating morphology in a tissue sample according to an embodiment of thedisclosed technology.

FIG. 2A shows a flow chart of a method for locating morphology in atissue sample according to an embodiment of the disclosed technology,which method may be carried out using the device of FIG. 1.

FIG. 2B is a continuation of FIG. 2A.

FIG. 3 shows a flow chart of a method for associating a name with amorphology in a tissue sample according to an embodiment of thedisclosed technology.

FIG. 4 shows a high-level block diagram of a device that may be used tocarry out the disclosed technology.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE DISCLOSED TECHNOLOGY

Embodiments of the disclosed technology are described below, withreference to the figures provided.

FIG. 1 shows a schematic block diagram of a system or a device forlocating morphology in a tissue sample according to an embodiment of thedisclosed technology.

As seen in FIG. 1, a device 10 according to the disclosed technologyincludes a processor 12, adapted to carry out instructions as describedin further detail hereinbelow. The device 10 may further include, or beassociated with, a data repository 14, such as a database. The databaseincludes superpixels, each taken from an image of a tissue sample takenfrom a mammalian body. Each of the superpixels in the database isassociated with a corresponding feature vector, and at least some of thesuperpixels and associated feature vectors are labeled with a specificname, such as a name of a morphology or pathology imaged in thesuperpixel and/or represented by the feature vector.

In some embodiments, the data repository 14 is local to device 10, andis in direct communication with processor 12. In other embodiments, datarepository 14 may be remote from processor 12, and may be functionallyassociated with the processor, or in communication therewith, via anetwork, such as a Local Area Network, for example local to a hospitalor research facility, or a Wide Area Network, such as the Internet oranother packet-switched network.

The processor 12 is further functionally associated with, or incommunication with, an imaging device providing an image of a tissuesample, such as, for example, a microscope 16. In some embodiments, theimage may be a live image, captured by microscope 16 from a suitableslide and delivered in real time to processor 12.

The processor 12 receives an image of a tissue sample, for example frommicroscope 16, and carried out instructions to convert at least onesuperpixel of the image into a feature vector representing features ofthe superpixel, to find a nearest match between the feature vectorrepresenting the superpixel and a feature vector stored in the datarepository 14, and to output a name suggestion of the superpixel basedon a name associated with the feature vector to which a nearest matchwas found. Further details relating to the functionality of processor 12and actions carried out thereby are provided hereinbelow with respect toFIG. 2.

The device 10 may further include at least one user interface 18,including an input mechanism 20 via which a user, or a viewer, mayprovide input to processor 12. For example, the viewer may provide aconfirmation of a name suggestion provided by the processor 12, asdescribed in further detail hereinbelow with respect to FIG. 2, or mayprovide a name suggestion to be associated with a specific superpixel orfeature vector, as described hereinbelow with respect to FIG. 3. In someembodiments, multiple users may provide input to processor 12, viamultiple user interfaces 18 and multiple input mechanism 20. The userinterface 18 may further include a display 22 for displaying informationto a viewer, such as a suggested name for a specific superpixel.

In some embodiments, the device 10 further includes, or is associatedwith, a density measuring component 24 which measures a density of namedmorphologies in a captured image. In some such embodiments, the densitymeasuring component 24 is functionally associated with display 22, suchthat relative densities of morphologies in part of an image may bepresented to the user or viewer. In some embodiments, the densitymeasuring component 24 may be a software thread running on processor 12,such as an image processing thread.

Reference is additionally made to FIG. 2, which shows a flow chart of amethod for locating morphology in a tissue sample according to anembodiment of the disclosed technology, which method may be carried outusing the device of FIG. 1.

As seen in FIG. 2 (split into FIGS. 2A and 2B), initially a tissuesample is obtained from a mammalian body, at step 200. The tissue samplemay be any suitable tissue sample, and may be obtained by any suitablemeans, such as during surgery, by a biopsy, and the like. A digitalimage of the tissue sample is obtained, or digitized, at step 202. Thismay be carried out using any suitable device, such as for example bymicroscope 16 (FIG. 1) or using any other suitable image capturingdevice.

At step 204, at least one superpixel of the digitized image is selected,for example by processor 12 (FIG. 1). The superpixel is then convertedinto a feature vector at step 206. The feature vector is a mathematicalrepresentation of features of the superpixel, such as to colordistribution, intensity of color, height distribution, texturedistribution, uniformity of color, and the like.

At step 208, the feature vector obtained in step 206 is compared to oneor more other feature vectors associated with other superpixels, to finda specific other feature vector to which the feature vector has thenearest match, or is most similar. Each of the other feature vectorsand/or other superpixels is further associated with a name, whichtypically represents a morphology or pathology of the tissue sample fromwhose image the other superpixel was obtained. In some embodiments, theother feature vector(s) may be previously obtained, and may be stored ina data repository, such as data repository 14 (FIG. 1). In otherembodiments, the other feature vector is obtained from another sample,using a method similar to that described with respect to steps 202 to206, and is named as described in further detail hereinbelow withrespect to FIG. 3.

The feature vector representation of the superpixel of the sample may becompared to the other feature vectors using any suitable metric oralgorithm known in the art, such as distance metrics, clustering metricsand algorithms, and the like.

In some embodiments steps 204 to 208, namely finding a superpixel,converting it to a feature vector, and comparing the feature vector toother feature vectors, are repeated for various possible superpixel ofthe image. In some such embodiments, the minimum block size of asuperpixel is a block size of 30×30 pixels.

Once a nearest match is found, at step 210 the system evaluates whetherthe matching feature vector has a name associated therewith. If a nameis assigned to the matching feature vector, at step 211 the nameassociated with the matching feature vector is assigned to the currentfeature vector. For example, the name may represent a morphology orpathology which is thought to be represented in the superpixel. The namemay be presented to the viewer or user at step 212, for example byprocessor 12 providing the suggested name to a viewer on a display 22 ofa user interface 18 (FIG. 1).

At step 214, the viewer or user may provide input relating to theproposed name, which input may be a confirmation of the proposed name(indicating that based on the captured image as seen by the viewer, thename correctly represents the morphology or pathology in thesuperpixel), or a rejection of the proposed name.

At step 216, the viewer's input is evaluated to determine whether or notit is a confirmation of the proposed name. If the received input is aconfirmation of the proposed name, at step 218 a naming counter isincreased. The threshold counter represents the number of times that amorphology has been correctly named.

On the other hand, if the received input is not a confirmation of theproposed name, at step 220 the naming counter is evaluated to determinewhether or not a threshold value has been reached, for example byprocessor 12 (FIG. 1). If the naming counter is greater than or equal tothe threshold value, the name assigned to the superpixel (and to themorphology represented thereby) may not be changed, and the lack ofconfirmation is ignored at step 222. Otherwise, if the naming counter issmaller than the predetermined threshold, the user may be prompted toprovide another name to associate with the superpixel, at step 224.

It is a particular feature of the disclosed technology that the system10 learns suitable names for specific morphologies and pathologies, byconfirmations provided by viewers to suggested names, as describedhereinabove. Additionally, use of the naming counter ensures that once aspecific morphology has been correctly named a sufficient number oftimes to show that the system has correctly learned to identify themorphology, a user may not change the name assigned to the morphology.As such, the system cannot “unlearn” what has been correctly learned,and a user, such as an inexperienced user, cannot damage or harm thefunctionality of the system by introducing inaccurate classifications ornames.

If at step 210 it is found that the matching feature vector has no nameassociated therewith, at step 225 the two matching superpixels arepresented to the user or viewer, for example on display 22 (FIG. 1), andthe user is prompted to provide a name to be associated with thematching superpixels. The name provided by the user is stored, inassociation with the two matching feature vectors and superpixels, forexample in data repository 14 (FIG. 1), at step 226.

In some embodiments, at step 227 the processor 12, or the densitymeasuring component 24 (FIG. 1), determines whether a section of theimage which has a high density of named morphologies. This may occurfollowing steps 204 to 224, in which one or more superpixels of theimage are identified as being associated with named morphologies.

In such embodiments, if a section with a high density of namedmorphologies is found, the section may be indicated to the viewer atstep 228. For example, the indication may be provided by zooming in of adisplay of the image to the high density section, outlining the highdensity section, and/or color coding the high density section.

In some embodiments, when the morphology named in step 210, or themorphology name provided in step 225, is associated with a specificclinical context, or when in the high density section multiplemorphology names are associated with a specific clinical context, anindication may be provided to the user that a specific type of medicalspecialty should be employed for a patient associated with the sample.For example, if the named morphology is associated with leukemia, anindication may be provided to the viewer that the patient from whom thesample was obtained should be referred to a hemato oncologist.

FIG. 3 shows a flow chart of a method for associating a name with amorphology in a tissue sample according to an embodiment of thedisclosed technology. As seen in FIG. 3, initially at step 300 a digitalimage of a tissue sample is obtained, the tissue sample being from amammalian source. In some embodiments, at step 302, the digital image ispresented to a user, and the user selects a superpixel of the image atstep 304, and associates a name with a morphology of the superpixel atstep 306. At step 307 the superpixel may be converted into a featurevector, and may be stored, together with the associated feature vectorand/or name, for example in data repository 14 (FIG. 1).

In some embodiments, when a specific user or viewer carries out steps300 to 306 multiple times with different samples, patterns of interestof the user or viewer are recognized at step 308, for example byprocessor 12 (FIG. 1). For example, such patterns of interest may berecognized based on any two or more of areas of the images where theuser zooms, areas of the images where the user pans, and/or areas of theimages where the user names regions or morphologies, or where the userselects superpixels.

In some such embodiments, when superpixels and/or morphologies need tobe identified in an additional digital image, the additional digitalimage is automatically divided into a plurality of superpixels at step310, for example each block of size 30×30 pixels is considered to be asuperpixel of the additional digital image. At step 312, specificsuperpixels from the plurality of superpixels are suggested to the userfor naming thereof or for implementation of the method of FIG. 2, basedon the recognized patterns of interest of the user, and naming of thesuggested superpixels may continue in step 306 of FIG. 3 (or in steps208 to 228 of FIG. 2).

In some embodiments, at step 314, some of said plurality of superpixelsmay be provided to at least one other user for naming thereof, and atstep 316 the other user may name the suggested superpixels as describedwith respect to step 306 FIG. 3 (or in steps 208 to 228 of FIG. 2). Insome embodiments, the at least one other user is a user having a patternof interest which overlaps with the pattern of interest of the user, orwhich is similar to the pattern of interest of the user. In someembodiments, the at least one other user is a user who is indicated tobe, or have, the same medical specialty as the user or a similar medicalspecialty to the user.

For example, if the first user is a hemato-oncologist interested inmorphologies representative of lymphoma, suggested superpixels may beprovided to another user which is interested in morphologiesrepresentative of lymphoma and/or to other hemato oncologists.

FIG. 4 shows a high-level block diagram of a device that may be used tocarry out the disclosed technology. Computing device 400 comprises aprocessor 450 that controls the overall operation of the device byexecuting the device's program instructions which define such operation.The device's program instructions may be stored in a storage device 420(e.g., magnetic disk, database) and loaded into memory 430 whenexecution of the console's program instructions is desired. Thus, thedevice's operation will be defined by the device's program instructionsstored in memory 430 and/or storage 420, and the console will becontrolled by processor 450 executing the console's programinstructions.

The device 400 also includes one or a plurality of input networkinterfaces for communicating with other devices via a network (e.g.,packet-switched data network). The device 400 further includes anelectrical input interface for receiving power and data from a powersource. A device 400 also includes one or more output network interfaces410 for communicating with other devices. Device 400 also includesinput/output 440, representing devices which allow for user interactionwith a computing device (e.g., touch display, keyboard, fingerprintreader etc.).

One skilled in the art will recognize that an implementation of anactual device will contain other components as well, and that FIG. 4 isa high level representation of some of the components of such a devicefor illustrative purposes. It should also be understood by one skilledin the art that the methods, systems and/or devices depicted in FIGS. 1through 3 may be implemented on a device such as is shown in FIG. 4.

While the disclosed technology has been taught with specific referenceto the above embodiments, a person having ordinary skill in the art willrecognize that changes can be made in form and detail without departingfrom the spirit and the scope of the disclosed technology. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. All changes that come within the meaning and rangeof equivalency of the claims are to be embraced within their scope.Combinations of any of the methods, systems, and devices describedhereinabove are also contemplated and within the scope of the disclosedtechnology.

1. A method of locating morphology in a tissue sample, comprises thesteps of: obtaining a first and second tissue sample, each removed froma mammalian body; digitizing an image of said first tissue sample andsaid second tissue sample; receiving a selection of at least onesuperpixel of said image of said first tissue sample; receiving a namedmorphology for said at least one superpixel and converting said at leastone superpixel into a first feature vector; extracting superpixels ofsaid image of said second tissue sample and converting each superpixelof said second image of said second tissue into second feature vectors;finding, within said second feature vectors of said second tissue, anearest match feature vector to said first feature vector; andindicating that a superpixel of said second image associated with saidnearest match feature vector is possibly a representation of said namedmorphology.
 2. The method of claim 1, further comprising steps of:exhibiting said superpixel associated with said nearest match featurevector to a viewer; and receiving confirmation from said viewer of saidsuperpixel associated with said nearest match feature vector is saidnamed morphology.
 3. The method of claim 2, wherein after said step ofreceiving confirmation from said viewer for said named morphology iscarried out a threshold number of minimum times, a name of said namedmorphology becomes unchangeable for future scanned superpixelsdetermined to be associated with said named morphology.
 4. The method ofclaim 3, wherein before said threshold number of minimum times, a nameof said pathology is changeable for at least some feature vectorsassociated with a superpixel of a morphology which was previouslyindicated as said named morphology.
 5. The method of claim 4, wherein asuperpixel comprises a minimum of block size of 30 by 30 pixels and eachpossible block of said second image is scanned to find a superpixelwhich has an associated feature vector closest to said first featurevector.
 6. The method of claim 4, further comprising receiving a thirdtissue sample having a third superpixel with a feature vector indicatedas being a nearest match to a feature vector found in one of said firstor said second tissue sample; wherein said third superpixel andcorresponding superpixels associated with said matching feature vectorof said first or said second tissue sample are unnamed; exhibiting atleast one of said third superpixel and said corresponding superpixels;requesting a name for said at least one of said third superpixel andsaid corresponding superpixels; and providing said name at a future timewhen a feature vector is found corresponding to said third superpixel.7. The method of claim 1, wherein said method is carried out multipletimes with a same viewer, each time said first and second tissue samplebeing replaced with additional said tissue samples, further comprisingthe steps of: recognizing patterns of interest by said same user basedon two or more of: areas of said images where said user zooms; areas ofsaid image where said user pans; areas of said images where user namesregions.
 8. The method of claim 7, wherein suggestions of superpixels toview are sent to said viewer based on said patterns of interest.
 9. Themethod of claim 8, wherein said suggestions are further made for asecond viewer based on said patterns of interest overlapping for saidviewer and said second viewer.
 10. The method of claim 8, wherein saidsuggestions are further made for additional viewers based on receivingan indication that said viewer and at least one additional viewer ofsaid additional viewers is in a same medical specialty as said viewer.11. The method of claim 1, further comprising a step of: determining asection of an image of said second tissue sample with a high density ofnamed morphologies; indicating to said viewer said high density section.12. The method of claim 11, wherein said indicating to said viewer saidhigh density section is carried out by way of one of: zooming in adisplay of said second tissue sample to said high density section;outlining said high density section; and color coding said high densitysection.
 13. The method of claim 1, wherein in second tissue sample, aplurality of additional named morphologies, including said namedmorphology, are determined to be related to a clinical context andindicating to said viewer that a particular type of medical specialtyshould be employed for a patient associated with said second tissuesample.
 14. The method of claim 1, wherein said second tissue sample isbeing viewed by said viewer in real-time using a microscope apparatus.15. A device for locating morphology in a tissue sample, comprising:storage of a plurality of feature vectors, each associated with aspecific named superpixel of a larger image of a tissue sample from amammalian body; a microscope outputting a live image of an additionaltissue sample; a processor carrying out instructions to: a) convert atleast one superpixel of said live image into a first feature vector; b)find a nearest match between said first feature vector and saidplurality of stored feature vectors; and c) output a first namesuggestion of said at least one superpixel of said live image based onsaid nearest match to one of said stored plurality of feature vectorsassociated with said specific named superpixel.
 16. The device of claim15, further comprising an input mechanism through which a recipient ofsaid name suggestion sends confirmation of said name suggestion; whereinupon receiving a confirmation of said name suggestion for said firstname a threshold number of minimum times, said first name becomespermanently associated with a particular morphology and is becomesunchangeable for future scanned superpixels determined to be associatedwith said named morphology.
 17. The device of claim 15, wherein said atleast one superpixel is a plurality of superpixels making up said liveimage and suggestions as to which superpixels of said plurality ofsuperpixels to view are indicated to said viewer based on a pattern ofinterest identified for said viewer.
 18. The device of claim 17, whereinsaid pattern of interest identified for said viewer is based onoverlapping selections of superpixels between said viewer and a secondviewer.
 19. The device of claim 15, wherein said suggestions are furthermade for additional viewers based on receiving an indication that saidviewer and at least one additional viewer of said additional viewers isin a same medical specialty as said viewer.
 20. The device of claim 15,further comprising a component measuring a density of named morphologiesin an image and a component exhibiting to a user relative densities ofat least one selection of part of said image.
 21. The device of claim20, wherein said device determines morphologies of greatest frequencycovering a plurality of superpixels and prompts said user to name saidmorphologies of greatest frequency.
 22. The device of claim 20, whereinsaid device determines regions comprising a plurality of superpixelswhere morphologies are unnamed and prompts said user to name saidregions.